Bayesian neural learning is an alternative learning method in which parameters or weights are (jointly) represented by a probability distribution rather than a point estimate that is usually returned by gradient learning methods [1]}. This probability distribution, the posterior, naturally accounts for uncertainty in parameter estimates, which is further propagated into the decision making process [2]}. Bayes' theorem is used as foundation for inference in Bayesian neural networks, and Markov chain Monte Carlo (MCMC) sampling methods [3]} are used for constructing the posterior distribution. Variational inference [4]} is another way to approximate the posterior distribution, which approximates an intractable posterior distribution by a tractable one. This makes it particularly suited to large data sets and models, and so it has been popular for autoencoders and neural networks [5]}, [6]}.
